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1.
Behav Res Methods ; 55(8): 4086-4098, 2023 12.
Artigo em Inglês | MEDLINE | ID: mdl-36357762

RESUMO

Synesthesia is a phenomenon where sensory stimuli or cognitive concepts elicit additional perceptual experiences. For instance, in a commonly studied type of synesthesia, stimuli such as words written in black font elicit experiences of other colors, e.g., red. In order to objectively verify synesthesia, participants are asked to choose colors for repeatedly presented stimuli and the consistency of their choices is evaluated (consistency test). Previously, there has been no publicly available and easy-to-use tool for analyzing consistency test results. Here, the R package synr is introduced, which provides an efficient interface for exploring consistency test data and applying common procedures for analyzing them. Importantly, synr also implements a novel method enabling identification of participants whose scores cannot be interpreted, e.g., who only give black or red color responses. To this end, density-based spatial clustering of applications with noise (DBSCAN) is applied in conjunction with a measure of spread in 3D space. An application of synr with pre-existing openly accessible data illustrating how synr is used in practice is presented. Also included is a comparison of synr's data validation procedure and human ratings, which found that synr had high correspondence with human ratings and outperformed human raters in situations where human raters were easily mislead. Challenges for widespread adoption of synr as well as suggestions for using synr within the field of synesthesia and other areas of psychological research are discussed.


Assuntos
Transtornos da Percepção , Humanos , Sinestesia , Percepção de Cores/fisiologia
2.
BMC Bioinformatics ; 23(1): 187, 2022 May 17.
Artigo em Inglês | MEDLINE | ID: mdl-35581558

RESUMO

The rapid global spread and dissemination of SARS-CoV-2 has provided the virus with numerous opportunities to develop several variants. Thus, it is critical to determine the degree of the variations and in which part of the virus those variations occurred. Therefore, in this study, methods that could be used to vectorize the sequence data, perform clustering analysis, and visualize the results were proposed using machine learning methods. To conduct this study, a total of 224,073 cases of SARS-CoV-2 sequence data were collected through NCBI and GISAID, and the data were visualized using dimensionality reduction and clustering analysis models such as T-SNE and DBSCAN. The SARS-CoV-2 virus, which was first detected, was distinguished from different variations, including Omicron and Delta, in the cluster results. Furthermore, it was possible to examine which codon changes in the spike protein caused the variants to be distinguished using feature importance extraction models such as Random Forest or Shapely Value. The proposed method has the advantage of being able to analyse and visualize a large amount of data at once compared to the existing tree-based sequence data analysis. The proposed method was able to identify and visualize significant changes between the SARS-CoV-2 virus, which was first detected in Wuhan, China, in December 2019, and the newly formed mutant virus group. As a result of clustering analysis using sequence data, it was possible to confirm the formation of clusters among various variants in a two-dimensional graph, and by extracting the importance of variables, it was possible to confirm which codon changes played a major role in distinguishing variants. Furthermore, since the proposed method can handle a variety of data sequences, it can be used for all kinds of diseases, including influenza and SARS-CoV-2. Therefore, the proposed method has the potential to become widely used for the effective analysis of disease variations.


Assuntos
COVID-19 , Magnoliopsida , Análise por Conglomerados , Códon , Aprendizado de Máquina , SARS-CoV-2/genética
3.
Sensors (Basel) ; 21(9)2021 Apr 22.
Artigo em Inglês | MEDLINE | ID: mdl-33922298

RESUMO

The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO2 emissions reduction) or the resulted values were far much higher (>50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier's removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection.

4.
Sensors (Basel) ; 20(18)2020 Sep 22.
Artigo em Inglês | MEDLINE | ID: mdl-32971798

RESUMO

Compared with the commonly used lidar and visual sensors, the millimeter-wave radar has all-day and all-weather performance advantages and more stable performance in the face of different scenarios. However, using the millimeter-wave radar as the Simultaneous Localization and Mapping (SLAM) sensor is also associated with other problems, such as small data volume, more outliers, and low precision, which reduce the accuracy of SLAM localization and mapping. This paper proposes a millimeter-wave radar SLAM assisted by the Radar Cross Section (RCS) feature of the target and Inertial Measurement Unit (IMU). Using IMU to combine continuous radar scanning point clouds into "Multi-scan," the problem of small data volume is solved. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is used to filter outliers from radar data. In the clustering, the RCS feature of the target is considered, and the Mahalanobis distance is used to measure the similarity of the radar data. At the same time, in order to alleviate the problem of the lower accuracy of SLAM positioning due to the low precision of millimeter-wave radar data, an improved Correlative Scan Matching (CSM) method is proposed in this paper, which matches the radar point cloud with the local submap of the global grid map. It is a "Scan to Map" point cloud matching method, which achieves the tight coupling of localization and mapping. In this paper, three groups of actual data are collected to verify the proposed method in part and in general. Based on the comparison of the experimental results, it is proved that the proposed millimeter-wave radar SLAM assisted by the RCS feature of the target and IMU has better accuracy and robustness in the face of different scenarios.

5.
Sensors (Basel) ; 19(4)2019 Feb 22.
Artigo em Inglês | MEDLINE | ID: mdl-30813288

RESUMO

The automated modal analysis (AMA) technique has attracted significant interest over the last few years, because it can track variations in modal parameters and has the potential to detect structural changes. In this paper, an improved density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean the abnormal poles in a stabilization diagram. Moreover, the optimal system model order is also discussed to obtain more stable poles. A numerical simulation and a full-scale experiment of an arch bridge are carried out to validate the effectiveness of the proposed algorithm. Subsequently, the continuous dynamic monitoring system of the bridge and the proposed algorithm are implemented to track the structural changes during the construction phase. Finally, the artificial neural network (ANN) is used to remove the temperature effect on modal frequencies so that a health index can be constructed under operational conditions.

6.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 36(4): 633-642, 2019 Aug 25.
Artigo em Chinês | MEDLINE | ID: mdl-31441265

RESUMO

The deoxyribonucleic acid (DNA) molecule damage simulations with an atom level geometric model use the traversal algorithm that has the disadvantages of quite time-consuming, slow convergence and high-performance computer requirement. Therefore, this work presents a density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm based on the spatial distributions of energy depositions and hydroxyl radicals (·OH). The algorithm with probability and statistics can quickly get the DNA strand break yields and help to study the variation pattern of the clustered DNA damage. Firstly, we simulated the transportation of protons and secondary particles through the nucleus, as well as the ionization and excitation of water molecules by using Geant4-DNA that is the Monte Carlo simulation toolkit for radiobiology, and got the distributions of energy depositions and hydroxyl radicals. Then we used the damage probability functions to get the spatial distribution dataset of DNA damage points in a simplified geometric model. The DBSCAN clustering algorithm based on damage points density was used to determine the single-strand break (SSB) yield and double-strand break (DSB) yield. Finally, we analyzed the DNA strand break yield variation trend with particle linear energy transfer (LET) and summarized the variation pattern of damage clusters. The simulation results show that the new algorithm has a faster simulation speed than the traversal algorithm and a good precision result. The simulation results have consistency when compared to other experiments and simulations. This work achieves more precise information on clustered DNA damage induced by proton radiation at the molecular level with high speed, so that it provides an essential and powerful research method for the study of radiation biological damage mechanism.


Assuntos
Algoritmos , Dano ao DNA , DNA/efeitos da radiação , Transferência Linear de Energia , Simulação por Computador , Método de Monte Carlo , Prótons
7.
Cells ; 13(17)2024 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-39273016

RESUMO

Super-resolution single-molecule localization microscopy (SMLM) of presynaptic active zones (AZs) and postsynaptic densities contributed to the observation of protein nanoclusters that are involved in defining functional characteristics and in plasticity of synaptic connections. Among SMLM techniques, direct stochastic optical reconstruction microscopy (dSTORM) depends on organic fluorophores that exert high brightness and reliable photoswitching. While multicolor imaging is highly desirable, the requirements necessary for high-quality dSTORM make it challenging to identify combinations of equally performing, spectrally separated dyes. Red-excited carbocyanine dyes, e.g., Alexa Fluor 647 (AF647) or Cy5, are currently regarded as "gold standard" fluorophores for dSTORM imaging. However, a recent study introduced a set of chemically modified rhodamine dyes, including CF583R, that promise to display similar performance in dSTORM. In this study, we defined CF583R's performance compared to AF647 and CF568 based on a nanoscopic analysis of Bruchpilot (Brp), a nanotopologically well-characterized scaffold protein at Drosophila melanogaster AZs. We demonstrate equal suitability of AF647, CF568 and CF583R for basal AZ morphometry, while in Brp subcluster analysis CF583R outperforms CF568 and is on par with AF647. Thus, the AF647/CF583R combination will be useful in future dSTORM-based analyses of AZs and other subcellularly located marker molecules and their role in physiological and pathophysiological contexts.


Assuntos
Drosophila melanogaster , Corantes Fluorescentes , Animais , Drosophila melanogaster/metabolismo , Corantes Fluorescentes/química , Processos Estocásticos , Proteínas de Drosophila/metabolismo , Microscopia de Fluorescência/métodos , Rodaminas/química
8.
Sci Rep ; 14(1): 23108, 2024 10 04.
Artigo em Inglês | MEDLINE | ID: mdl-39367045

RESUMO

Hallucinations can have rather heterogeneous aetiology and presentation. This inspired the concept of different subtypes based on symptom profiles, especially in the field of auditory hallucinations. As many people experience hallucinations in more than one sensory modality, it seems important to investigate potential hallucination subtypes across different sensory modalities. We assessed the content of hallucinations as part of a large survey among the general Dutch population (n = 10,448) using the Questionnaire for Psychotic Experiences. Based on their descriptions, thematic categories were created in a data-driven cluster analysis. 2594 participants who experienced hallucinations over the past week that contained at least 2 different thematic categories were selected. Clustering of their hallucination content was performed with the HDBSCAN method. We identified 4 clusters, i.e., subtypes, which can be typified as 1. hallucinations of foul odors, 2. complex visual scenes, 3. a vast variety of rather common hallucinations possibly related to heightened alertness, and 4. possibly bereavement hallucinations. The bereavement subtype showed an increase in emotional loneliness and the presence of delusions. Our findings suggest that the content of hallucinations can be informative, especially when investigated across sensory modalities. Such subtypes may help to better understand their underlying mechanisms.


Assuntos
Alucinações , Alucinações/epidemiologia , Humanos , Feminino , Masculino , Adulto , Inquéritos e Questionários , Pessoa de Meia-Idade , Análise por Conglomerados , Países Baixos/epidemiologia , Idoso , Adulto Jovem , Adolescente , Delusões
9.
Methods Mol Biol ; 2800: 167-187, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38709484

RESUMO

Analyzing the dynamics of mitochondrial content in developing T cells is crucial for understanding the metabolic state during T cell development. However, monitoring mitochondrial content in real-time needs a balance of cell viability and image resolution. In this chapter, we present experimental protocols for measuring mitochondrial content in developing T cells using three modalities: bulk analysis via flow cytometry, volumetric imaging in laser scanning confocal microscopy, and dynamic live-cell monitoring in spinning disc confocal microscopy. Next, we provide an image segmentation and centroid tracking-based analysis pipeline for automated quantification of a large number of microscopy images. These protocols together offer comprehensive approaches to investigate mitochondrial dynamics in developing T cells, enabling a deeper understanding of their metabolic processes.


Assuntos
Citometria de Fluxo , Microscopia Confocal , Mitocôndrias , Análise de Célula Única , Linfócitos T , Citometria de Fluxo/métodos , Mitocôndrias/metabolismo , Análise de Célula Única/métodos , Linfócitos T/metabolismo , Linfócitos T/citologia , Microscopia Confocal/métodos , Animais , Processamento de Imagem Assistida por Computador/métodos , Humanos , Camundongos , Dinâmica Mitocondrial
10.
Front Mol Neurosci ; 17: 1431549, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-39296283

RESUMO

Alpha-synuclein (aSyn) aggregates in the central nervous system are the main pathological hallmark of Parkinson's disease (PD). ASyn aggregates have also been detected in many peripheral tissues, including the skin, thus providing a novel and accessible target tissue for the detection of PD pathology. Still, a well-established validated quantitative biomarker for early diagnosis of PD that also allows for tracking of disease progression remains lacking. The main goal of this research was to characterize aSyn aggregates in skin biopsies as a comparative and quantitative measure for PD pathology. Using direct stochastic optical reconstruction microscopy (dSTORM) and computational tools, we imaged total and phosphorylated-aSyn at the single molecule level in sweat glands and nerve bundles of skin biopsies from healthy controls (HCs) and PD patients. We developed a user-friendly analysis platform that offers a comprehensive toolkit for researchers that combines analysis algorithms and applies a series of cluster analysis algorithms (i.e., DBSCAN and FOCAL) onto dSTORM images. Using this platform, we found a significant decrease in the ratio of the numbers of neuronal marker molecules to phosphorylated-aSyn molecules, suggesting the existence of damaged nerve cells in fibers highly enriched with phosphorylated-aSyn molecules. Furthermore, our analysis found a higher number of aSyn aggregates in PD subjects than in HC subjects, with differences in aggregate size, density, and number of molecules per aggregate. On average, aSyn aggregate radii ranged between 40 and 200 nm and presented an average density of 0.001-0.1 molecules/nm2. Our dSTORM analysis thus highlights the potential of our platform for identifying quantitative characteristics of aSyn distribution in skin biopsies not previously described for PD patients while offering valuable insight into PD pathology by elucidating patient aSyn aggregation status.

11.
Comput Struct Biotechnol J ; 19: 1302-1311, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33738079

RESUMO

Local 3D-structural differences in homologous proteins contribute to functional diversity observed in a superfamily, but so far received little attention as bioinformatic analysis was usually carried out at the level of amino acid sequences. We have developed Zebra3D - the first-of-its-kind bioinformatic software for systematic analysis of 3D-alignments of protein families using machine learning. The new tool identifies subfamily-specific regions (SSRs) - patterns of local 3D-structure (i.e. single residues, loops, or secondary structure fragments) that are spatially equivalent within families/subfamilies, but are different among them, and thus can be associated with functional diversity and function-related conformational plasticity. Bioinformatic analysis of protein superfamilies by Zebra3D can be used to study 3D-determinants of catalytic activity and specific accommodation of ligands, help to prepare focused libraries for directed evolution or assist development of chimeric enzymes with novel properties by exchange of equivalent regions between homologs, and to characterize plasticity in binding sites. A companion Mustguseal web-server is available to automatically construct a 3D-alignment of functionally diverse proteins, thus reducing the minimal input required to operate Zebra3D to a single PDB code. The Zebra3D + Mustguseal combined approach provides the opportunity to systematically explore the value of SSRs in superfamilies and to use this information for protein design and drug discovery. The software is available open-access at https://biokinet.belozersky.msu.ru/Zebra3D.

12.
J Adv Res ; 18: 9-18, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30788173

RESUMO

Large-scale comparative studies of DNA fingerprints prefer automated chip capillary electrophoresis over conventional gel planar electrophoresis due to the higher precision of the digitalization process. However, the determination of band sizes is still limited by the device resolution and sizing accuracy. Band matching, therefore, remains the key step in DNA fingerprint analysis. Most current methods evaluate only the pairwise similarity of the samples, using heuristically determined constant thresholds to evaluate the maximum allowed band size deviation; unfortunately, that approach significantly reduces the ability to distinguish between closely related samples. This study presents a new approach based on global multiple alignments of bands of all samples, with an adaptive threshold derived from the detailed migration analysis of a large number of real samples. The proposed approach allows the accurate automated analysis of DNA fingerprint similarities for extensive epidemiological studies of bacterial strains, thereby helping to prevent the spread of dangerous microbial infections.

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